Biology subjects

Ge, T.

Publications and source records attributed to Ge, T..

29 records · Page 2Linked to original sources

bioRxiv2021

Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior

Resting-state functional MRI (rs-fMRI) allows estimation of individual-specific cortical parcellations. We have previously developed a multi-session hierarchical Bayesian model (MS-HBM) for estimating high-quality individual-specific network-level parcellations. Here, we extend the model to estimate individual-specific areal-level parcellations. While network-level parcellations comprise spatially distributed networks spanning the cortex, the consensus is that areal-level parcels should be spatially localized, i.e., should not span multiple lobes. There is disagreement about whether areal-level parcels should be strictly contiguous or comprise multiple non-contiguous components, therefore we considered three areal-level MS-HBM variants spanning these range of possibilities. Individual-specific MS-HBM parcellations estimated using 10min of data generalized better than other approaches using 150min of data to out-of-sample rs-fMRI and task-fMRI from the same individuals. Resting-state functional connectivity (RSFC) derived from MS-HBM parcellations also achieved the best behavioral prediction performance. Among the three MS-HBM variants, the strictly contiguous MS-HBM (cMS-HBM) exhibited the best resting-state homogeneity and most uniform within-parcel task activation. In terms of behavioral prediction, the gradient-infused MS-HBM (gMS-HBM) was numerically the best, but differences among MS-HBM variants were not statistically significant. Overall, these results suggest that areal-level MS-HBMs can capture behaviorally meaningful individual-specific parcellation features beyond group-level parcellations. Multi-resolution trained models and parcellations are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Kong2022_ArealMSHBM).

neuroscience↗

bioRxiv2020

Heritability of individualized cortical network topography

Human cortex is patterned by a complex and interdigitated web of large-scale functional networks. Recent methodological breakthroughs reveal variation in the size, shape, and spatial topography of cortical networks across individuals. While spatial network organization emerges across development, is stable over time, and predictive of behavior, it is not yet clear to what extent genetic factors underlie inter-individual differences in network topography. Here, leveraging a novel non-linear multi-dimensional estimation of heritability, we provide evidence that individual variability in the size and topographic organization of cortical networks are under genetic control. Using twin and family data from the Human Connectome Project (n=1,023), we find increased variability and reduced heritability in the size of heteromodal association networks (h2: M=0.33, SD=0.071), relative to unimodal sensory/motor cortex (h2: M=0.44, SD=0.051). We then demonstrate that the spatial layout of cortical networks is influenced by genetics, using our multi-dimensional estimation of heritability (h2-multi; M=0.14, SD=0.015). However, topographic heritability did not differ between heteromodal and unimodal networks. Genetic factors had a regionally variable influence on brain organization, such that the heritability of network topography was greatest in prefrontal, precuneus, and posterior parietal cortex. Taken together, these data are consistent with relaxed genetic control of association cortices relative to primary sensory/motor regions, and have implications for understanding population-level variability in brain functioning, guiding both individualized prediction and the interpretation of analyses that integrate genetics and neuroimaging. SignificanceThe widespread use of population-average cortical parcellations has provided important insights into broad properties of human brain organization. However, the size, location, and spatial arrangement of regions comprising functional brain networks can vary substantially across individuals. Here, we demonstrate considerable heritability in both the size and spatial organization of individual-specific network topography across cortex. Genetic factors had a regionally variable influence on brain organization, such that heritability in network size, but not topography, was greater in unimodal relative to heteromodal cortices. These data suggest individual-specific network parcellations may provide an avenue to understand the genetic basis of variation in human cognition and behavior.

neuroscience↗

bioRxiv2019

A two-stage approach to identifying and validating modifiable factors for the prevention of depression

BackgroundAlthough depression is recognized as the leading cause of disability worldwide, decades of research have identified few actionable preventive factors. Using phenotypic and genomic data from the UK Biobank, we took advantage of a unique opportunity to screen a wide range of potentially modifiable factors that could offset known risk factors for depression.\n\nMethodsWe curated baseline data on more than 100 lifestyle and environmental factors in participants lives, including behavioral (e.g., exercise, sleep, media use, diet), social (e.g., support, activities), and environmental (e.g., greenspace, pollution) variables. In a follow-up survey, participants reported on their traumatic life experiences and mental health, including depression. Polygenic risk scores for depression were generated based on large-scale genome-wide association results. Excluding those meeting criteria for depression at baseline, we identified at-risk individuals at high predicted probability (> 90th percentile) for clinically significant depression at follow-up based on their (i) polygenic risk, or (ii) reported traumatic life events. Using a factors-wide design corrected for multiple testing and adjusted for potential confounders, we identified modifiable factors associated with follow-up depression in the full sample and among at-risk individuals. Using a two-sample Mendelian randomization (MR) design, we then examined which significant factors showed potential causal influences on depression risk, or vice versa.\n\nResultsA range of baseline modifiable factors were prospectively associated with follow-up depression, including factors related to social engagement, physical activity, media use, and diet. MR follow-up analyses provided further support for the effects of social support-seeking, TV use, and other factors on depression risk.\n\nConclusionAs the field increasingly quantifies the role of genetic factors in complex conditions such as depression, knowledge of modifiable factors that could offset ones genetic risk has become highly relevant. Here, we present an approach to screening for potentially modifiable factors that may offset the risk of depression in general and among at-risk individuals. In light of the burden of disease associated with depression and the urgent need for actionable preventive strategies, this approach could help prioritize candidates for follow-up studies including clinical trials for depression prevention.

genetics↗

bioRxiv2019

A schizophrenia risk locus alters brain metal transport and plasma glycosylation

A common missense variant in SLC39A8 is convincingly associated with schizophrenia and several additional phenotypes. Homozygous loss-of-function mutations in SLC39A8 result in undetectable serum manganese (Mn) and a Congenital Disorder of Glycosylation (CDG) due to the exquisite sensitivity of glycosyltransferases to Mn concentration. Here, we identified several Mn-related changes in human carriers of the common SLC39A8 missense allele. Analysis of structural brain MRI scans showed a dose-dependent change in the ratio of T2w to T1w signal in several regions. Comprehensive trace element analysis confirmed a specific reduction of only serum Mn, and plasma protein N-glycome profiling revealed reduced complexity and branching. N-glycome profiling from two individuals with SLC39A8-CDG showed similar but more severe alterations in branching that improved with Mn supplementation, suggesting that the common variant exists on a spectrum of hypofunction with potential for reversibility. Characterizing the functional impact of this variant will enhance our understanding of schizophrenia pathogenesis and identify novel therapeutic targets and biomarkers of disease. SummaryA common variant in the manganese transporter SLC39A8 is associated with numerous phenotypes including schizophrenia. Mealer et. al. presents an in-depth analysis of brain MRI and plasma glycomics in human carriers of the common variant, identifying several manganese-related changes with potential for diagnostic and therapeutic biomarker development.

biochemistry↗

bioRxiv2019

An atlas on causal role of circulating vitamin D concentrations in human complex traits and diseases

Vitamin D has been associated with a variety of human complex traits and diseases in observational studies, but a causal relationship remains unclear. To examine a putative causal effect of vitamin D across phenotypic domains and disease categories, we conducted Mendelian randomization (MR) analyses using genetic instruments associated with circulating 25-hydroxyvitamin D [25(OH)D] concentrations. We leveraged genome-wide significant 25(OH)D-associated SNPs (N=138) from a meta-analysis combining a vitamin D GWAS conducted in 401,460 white British UK Biobank (UKBB) participants and an independent vitamin D GWAS including 42,274 samples of European ancestry, and examined 190 large-scale health-related GWAS spanning a broad spectrum of complex traits, diseases and biomarkers. We applied multiple MR methods to estimate the causal effect of vitamin D while testing and controlling for potential biases from horizontal pleiotropy. Consistent with previous findings, genetically predicted increased 25(OH)D levels significantly decreased the risk of multiple sclerosis (OR=0.824; 95%CI=0.689-0.986). The protective effect estimate was consistent across different MR methods and four different multiple sclerosis GWAS with varying sample sizes and genotyping platforms. On the contrary, we found limited evidence in support of a causal effect of 25(OH)D on anthropometric traits, obesity, cognitive function, sleep behavior, breast and prostate cancer, and autoimmune, cardiovascular, metabolic, neurological and psychiatric traits and diseases, and blood biomarkers. Our results may inform ongoing and future randomized clinical trials of vitamin D supplementation.

genetics↗

bioRxiv2019

Global Signal Regression Strengthens Association between Resting-State Functional Connectivity and Behavior

Global signal regression (GSR) is one of the most debated preprocessing strategies for resting-state functional MRI. GSR effectively removes global artifacts driven by motion and respiration, but also discards globally distributed neural information and introduces negative correlations between certain brain regions. The vast majority of previous studies have focused on the effectiveness of GSR in removing imaging artifacts, as well as its potential biases. Given the growing interest in functional connectivity fingerprinting, here we considered the utilitarian question of whether GSR strengthens or weakens associations between resting-state functional connectivity (RSFC) and multiple behavioral measures across cognition, personality and emotion. By applying the variance component model to the Brain Genomics Superstruct Project (GSP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 47% across 23 behavioral measures after GSR. In the Human Connectome Project (HCP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 40% across 58 behavioral measures, when GSR was applied after ICA-FIX de-noising. To ensure generalizability, we repeated our analyses using kernel regression. GSR improved behavioral prediction accuracies by an average of 64% and 12% in the GSP and HCP datasets respectively. Importantly, the results were consistent across methods. A behavioral measure with greater RSFC-explained variance (using the variance component model) also exhibited greater prediction accuracy (using kernel regression). A behavioral measure with greater improvement in behavioral variance explained after GSR (using the variance component model) also enjoyed greater improvement in prediction accuracy after GSR (using kernel regression). Furthermore, GSR appeared to benefit task performance measures more than self-reported measures. Since GSR was more effective at removing motion-related and respiratory-related artifacts, GSR-related increases in variance explained and prediction accuracies were unlikely the result of motion-related or respiratory-related artifacts. However, it is worth emphasizing that the current study focused on whole-brain RSFC, so it remains unclear whether GSR improves RSFC-behavioral associations for specific connections or networks. Overall, our results suggest that at least in the case for young healthy adults, GSR strengthens the associations between RSFC and most (although not all) behavioral measures. Code for the variance component model and ridge regression can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/preprocessing/Li2019_GSR. HighlightsO_LIGlobal signal regression improves RSFC-behavior associations C_LIO_LIGlobal signal regression improves RSFC-based behavioral prediction accuracies C_LIO_LIImprovements replicated across two large-scale datasets and methods C_LIO_LITask-performance measures enjoyed greater improvements than self-reported ones C_LIO_LIGSR beneficial even after ICA-FIX C_LI

neuroscience↗

bioRxiv2018

The transcriptional landscape of cortical interneurons underlies in-vivo brain function and schizophrenia risk

Inhibitory interneurons orchestrate information flow across cortex and are implicated in psychiatric illness. Although classes of interneurons have unique functional properties and spatial distributions throughout the brain, the relative influence of interneuron subtypes on brain function, cortical specialization, and illness risk remains elusive. Here, we demonstrate stereotyped organizational properties of somatostatin and parvalbumin related transcripts within human and non-human primates. Interneuron spatial distributions recapitulate cortico-striato-thalamic functional networks and track regional differences in functional MRI signal amplitude. In the general population (n=9,627), parvalbumin-linked genes account for an enriched proportion of genome-wide heritable variance in in-vivo functional MRI signal amplitude. This relationship is spatially dependent, following the topographic organization of parvalbumin expression in independent post-mortem brain tissue. Finally, genetic risk for schizophrenia is enriched among interneuron-linked genes and predictive of cortical signal amplitude in parvalbumin-biased regions. These data indicate that the molecular genetic basis of resting-state brain function across cortex is shaped by the spatial distribution of interneuron-related transcripts and underlies individual differences in risk for schizophrenia.\n\nKey FindingsO_LISpatial distributions of somatostatin (SST) and parvalbumin (PVALB) are negatively correlated in mature human and non-human primate cortex, paralleling patterns observed in utero.\nC_LIO_LISST and PVALB are differentially expressed within distinct limbic and somato/motor cortico-striato-thalamic networks, respectively.\nC_LIO_LIIn-vivo resting-state signal amplitude is heritable in the general population and tracks relative SST/PVALB expression across cortex.\nC_LIO_LISingle-nucleotide polymorphisms tied to PVALB-related genes account for an enriched proportion of the heritable variance in resting-state signal amplitude.\nC_LIO_LIPVALB-mediated heritability of resting-state signal amplitude in the general population is spatially heterogeneous, mirroring the cortical expression of PVALB in independent post-mortem brain tissue.\nC_LIO_LIPolygenic risk for schizophrenia is enriched among interneuron-linked genes and predicts resting-state signal amplitude in a manner that also follows the cortical expression of PVALB.\nC_LI

neuroscience↗

bioRxiv2018

Polygenic Prediction via Bayesian Regression and Continuous Shrinkage Priors

Polygenic prediction has shown promise in identifying individuals at high risk for complex diseases, and may become clinically useful as the predictive performance of polygenic risk scores (PRS) improves. Here, we present PRS-CS, a novel polygenic prediction method that infers posterior SNP effect sizes using GWAS summary statistics and an external linkage disequilibrium (LD) reference panel. PRS-CS utilizes a high-dimensional Bayesian regression framework, and is distinct from previous work by placing a continuous shrinkage (CS) prior on SNP effect sizes, which is robust to varying genetic architectures, provides substantial computational advantages, and enables multivariate modeling of local LD patterns. Simulation studies using data from the UK Biobank show that PRS-CS outperforms existing methods across a wide range of effect size distributions, especially when the training sample size is large. We apply PRS-CS to predict six complex diseases and six quantitative traits in the Partners HealthCare Biobank, and further demonstrate the improvement of PRS-CS in prediction accuracy over alternative methods.

genetics↗

bioRxiv2018

Altered Bacteria-Fungi Inter-Kingdom Network in Gut of Ankylosing Spondylitis Patients

Intestinal bacterial dysbiosis has been increasingly linked to Ankylosing Spondylitis (AS), which is a prototypic and best studied subtype of Spondyloarthritis (SpA). Fungi and bacteria coexist in human gut and interact with each other, although they have been shown to contribute actively to health or diseases, no studies have investigated whether fungal microbiota in AS patients is perturbed. In this study, fecal samples of 22 AS patients, with clinical and radiographic assessments, and 16 healthy controls (HCs) were collected to systematically characterize the gut microbiota and mycobiota in AS patients by 16S rDNA and ITS2-based DNA sequencing. The relationships between therapeutic regimens, disease activity, radiographic damage of AS and gut micro/mycobiome were investigated. Our results showed a distinct mycobiota pattern in AS in addition to microbiota dysbiosis. The gut mycobiome of AS patients was characterized by higher taxonomic levels of Ascomycota, especially the class of Dothideomycetes, and decreased abundance of Basidiomycota, which was mainly contributed by the decease of Agaricales. Compared to HCs, changing of the ITS2/16S biodiversity ratio, and bacteria-fungi interkingdom network were observed in AS patients. Alteration of gut mycobiota was associated with different therapeutic regimens, disease activity, as well as different degrees of radiographic damage. Moreover, we unraveled a disease-specific interkingdom network alteration in AS. Finally, we also identified some trends suggesting that different therapeutic regimens may induce changing of both bacterial and fungal microbiota in AS.\n\nIMPORTANCEHuman gut is colonized by diverse fungi (mycobiome), and they have long been suspected in the pathogenesis of Spondyloarthritis (SpA). Our study unraveled a disease-specific interkingdom network alteration in AS, suggesting that fungi, or the interkingdom interactions between bacteria and fungi, may play an essential role in AS development. However, limited by sample size and indeep mechanism studies, further large scale investigations on the characterization of gut mycobiome in AS patients are needed to form a foundation for research into the relationship between mycobiota dysbiosis and AS development.

microbiology↗

bioRxiv2018

A Polygenic Score for Higher Educational Attainment is Associated with Larger Brains

People who score higher on intelligence tests tend to have larger brains. Twin studies suggest the same genetic factors influence both brain size and intelligence. This has led to the hypothesis that genetics influence intelligence partly by contributing to development of larger brains. We tested this hypothesis with molecular genetic data using discoveries from a genome-wide association study (GWAS) of educational attainment, a correlate of intelligence. We analyzed genetic, brain imaging, and cognitive test data from the UK Biobank, the Dunedin Study, the Brain Genomics Superstruct Project (GSP), and the Duke Neurogenetics Study (DNS) (combined N=8,271). We measured genetics using polygenic scores based on published GWAS. We conducted meta-analysis to test associations among participants genetics, total brain volume (i.e., brain size), and cognitive test performance. Consistent with previous findings, participants with higher polygenic scores achieved higher scores on cognitive tests, as did participants with larger brains. Participants with higher polygenic scores also had larger brains. We found some evidence that brain size partly mediated associations between participants education polygenic scores and their cognitive test performance. Effect-sizes were larger in the population-based UK Biobank and Dunedin samples than in the GSP and DNS samples. Sensitivity analysis suggested this effect-size difference partly reflected restricted range of cognitive performance in the GSP and DNS samples. Recruitment and retention of population-representative samples should be a priority for neuroscience research. Findings suggest promise for studies integrating GWAS discoveries with brain imaging data to understand neurobiology linking genetics with individual differences in cognitive performance.

neuroscience↗

bioRxiv2018

The Shared Genetic Basis of Human Fluid Intelligence and Brain Morphology

Individual differences in educational attainment are linked to differences in intelligence, and predict important social, economic and health outcomes. Previous studies have found common genetic factors that influence educational achievement, cognitive performance and total brain volume (i.e., brain size). Here, in a large sample of participants from the UK Biobank, we investigate the shared genetic basis between educational attainment and fine-grained cerebral cortical morphological features, and associate this genetic variation with a related aspect of cognitive ability. Importantly, we execute novel statistical methods that enable high-dimensional genetic correlation analysis, and compute high-resolution surface maps for the genetic correlations between educational attainment and vertex-wise morphological measurements. We conduct secondary analyses, using the UK Biobank verbal-numerical reasoning score, to confirm that variation in educational attainment that is genetically correlated with cortical morphology is related to differences in cognitive performance. Our analyses reveal the genetic overlap between cognitive ability and cortical thickness measurements in bilateral primary motor cortex and predominantly left superior temporal cortex and proximal regions. These findings may contribute to our understanding of the neurobiology that connects genetic variation to individual differences in educational attainment and cognitive performance.

genetics↗